Data enlargement for big data analytics and system identification

Abstract

The present invention may include a computer receives raw data. The computer converts the raw data into a dataset, where the dataset comprises independent variables and dependent variables. Then, the computer clusters the dataset to determine a corresponding target value to each of a plurality of clusters. The computer constructs a nonlinear programming problem based on a prior experience and generates an enlarged dataset by solving the nonlinear programming problem.

What is claimed is: 
     
         1 . A processor-implemented method for data enlargement, the method comprising:
 receiving raw data;   converting the raw data into a dataset, wherein the dataset comprises independent variables and dependent variables;   clustering the dataset to determine a corresponding target value to each of a plurality of clusters;   constructing a nonlinear programming problem from the independent variables and the corresponding target value to each of the plurality of clusters based on a prior experience; and   generating an enlarged dataset by solving the nonlinear programming problem.   
     
     
         2 . The method of  claim 1 , further comprising:
 applying the enlarged dataset for a big data advanced analysis, wherein the big data advanced analysis uses a neural network algorithm requiring the enlarged dataset.   
     
     
         3 . The method of  claim 1 , wherein constructing a nonlinear programming problem further comprises:
 determining, automatically, a corresponding nonlinear programming problem to the dataset from the nonlinear programming problems database based on parameters of the dataset.   
     
     
         4 . The method of  claim 1 , wherein clustering the dataset to determine the corresponding target value to each of the plurality of clusters uses a hierarchical clustering that is based on a background-generated distance matrix. 
     
     
         5 . The method of  claim 1 , wherein the prior experience is based on nonlinear mathematical relations between the dependent variables and the independent variables of the dataset. 
     
     
         6 . The method of  claim 1 , wherein the nonlinear programming problem based on the prior experience is a corrosion rate, nonlinear programming problem. 
     
     
         7 . The method of  claim 1 , wherein generating the enlarged dataset by solving the nonlinear programming problem further comprises using the corresponding target value as an input to the nonlinear programming problem. 
     
     
         8 . A computer system for data enlargement, the computer system comprising:
 one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:   receiving raw data;   converting the raw data into a dataset, wherein the dataset comprises independent variables and dependent variables;   clustering the dataset to determine a corresponding target value to each of a plurality of clusters;   constructing a nonlinear programming problem from the independent variables and the corresponding target value to each of the plurality of clusters based on a prior experience; and   generating an enlarged dataset by solving the nonlinear programming problem.   
     
     
         9 . The computer system of  claim 8 , further comprising applying the enlarged dataset for a big data advanced analysis, wherein the big data advanced analysis uses a deep neural network algorithm requiring the enlarged dataset. 
     
     
         10 . The computer system of  claim 8 , wherein constructing a nonlinear programming problem further comprises determining, automatically, a corresponding nonlinear programming problem to the dataset from the nonlinear programming problems database based on parameters of the dataset. 
     
     
         11 . The computer system of  claim 8 , wherein clustering the dataset to determine the corresponding target value to each of the plurality of clusters uses a hierarchical clustering that is based on a background-generated distance matrix. 
     
     
         12 . The computer system of  claim 8 , wherein the prior experience is based on nonlinear mathematical relations between the dependent variables and the independent variables of the dataset. 
     
     
         13 . The computer system of  claim 8 , wherein the nonlinear programming problem based on the prior experience is a corrosion rate, nonlinear programming problem. 
     
     
         14 . The computer system of  claim 8 , wherein generating the enlarged dataset by solving the nonlinear programming problem further comprises using the corresponding target value as an input to the nonlinear programming problem. 
     
     
         15 . A computer program product for data enlargement, the computer program product comprising:
 one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor, the program instructions comprising:   program instructions to receive raw data;   program instructions to convert the raw data into a dataset, wherein the dataset comprises independent variables and dependent variables;   program instructions to cluster the dataset to determine a corresponding target value to each of a plurality of clusters;   program instructions to construct a nonlinear programming problem from the independent variables and the corresponding target value to each of the plurality of clusters based on a prior experience; and   program instructions to generate an enlarged dataset by solving the nonlinear programming problem.   
     
     
         16 . The computer program product of  claim 15 , further comprising applying the enlarged dataset for a big data advanced analysis, wherein the big data advanced analysis uses a deep neural network algorithm requiring the enlarged dataset. 
     
     
         17 . The computer program product of  claim 15 , wherein program instructions to construct the nonlinear programming problem further comprises program instructions to determine, automatically, a corresponding nonlinear programming problem to the dataset from the nonlinear programming problems database based on parameters of the dataset. 
     
     
         18 . The computer program product of  claim 15 , wherein clustering the dataset to determine the corresponding target value to each of the plurality of clusters uses a hierarchical clustering that is based on a background-generated distance matrix. 
     
     
         19 . The computer program product of  claim 15 , wherein the prior experience is based on nonlinear mathematical relations between the dependent variables and the independent variables of the dataset. 
     
     
         20 . The computer program product of  claim 15 , wherein the nonlinear programming problem based on the prior experience is a corrosion rate, nonlinear programming problem.